Hedge your bets with Probabilistic Forecasting

Jeff Bodenstab on December 13, 2018

We're big advocates of probabilistic forecasting (also known as stochastic forecasting). Understanding and employing this relatively simple principle can take your forecasting and supply chain planning from ‘good to great’.


For example: the horse “Red Rum” will win the Grand National. If "Red Rum" is the most successful racehorse of all time, you might place a single bet on him to win. In the world of supply chain planning, this kind of prediction is called a ‘single number’ forecast. With this, aided by basic systems such as spreadsheets or legacy planning systems) planners can forecast one number for a particular item.

Single number forecasting can work in these circumstances if you are confident that an established pattern will be repeated – such as with fast-moving, commodity items. For example, you might have 3 years of history of selling 100 standard USB chargers every week, give or take a few. In this case, forecasting 100 units is a pretty safe bet.

However, most products aren’t like that, just like most racehorses aren’t like "Red Rum". Even the most successful, healthy horses with good jockeys are subject to many unforeseeable variables that affect their actual outcome. They could have a collision, develop a sudden injury, or simply have an off day, so serious gamblers often review the range of possible outcomes and then apply their own knowledge before settling on a bet. They may also place multiple bets to ‘hedge’ against losses that would result from making a single bet.

This scenario is somewhat analogous to probabilistic forecasting. In supply chain planning, advanced algorithms are used to analyse multiple demand variables to identify the probabilities of a range of possible outcomes, one of which is the most likely. It’s a much more reliable way to make predictions where demand patterns are variable, where there’s limited order history as in the case of new product introduction, or when factors like seasonality come into play.

Even if aggregate weekly or monthly demand for an item stays relatively consistent, when you drill down into daily demand for that item by location, there is usually considerable volatility at this more granular level. In a distribution network, looking at aggregate demand is not enough. To meet service levels, you need a plan that ensures you get the right number of items to the right locations.

A probabilistic forecast that takes uncertainty into account helps you manage risk. It’s not just about improving average demand predictions but assessing the entire range of possible outcomes including demand volatility, which has the biggest impact on service levels.

With probabilistic forecasting, you still get one number that’s associated with the highest probability. However, banded around this number you get a range of other possible outcomes, each with a different probability attached.

Returning to the horse racing analogy, betting on "Red Rum" is a safe bet but where there’s little risk, there’s also less reward. Similarly, in business, there’s usually less upside in selling predictable commodity products. Most thriving companies profit from carrying ‘long-tail’ products in their portfolio. Viewed in this light, probabilistic forecasting is much more than a nerdy statistical method. It allows you to consistently place better inventory bets than your competitors for those harder-to-forecast items.

By freeing up working capital and improving service levels at the same time, this tried and tested approach can provide the sustainable competitive advantage needed to take your business from good to great.

When supply plans or safety stocks are based on wrong assumptions about demand uncertainty, targets cannot be reached and supply chains go into firefighting mode. Trust in the planning process erodes. When planners stop trusting forecasts they usually err on the side of holding too much safety stock and this leads to excessive costs, waste and obsolescence. We believe it's better to hedge your bets through probabilistic forecasting.

Topics: Inventory Optimization, inventory, long tail

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